Introduction

Progesterone and estrogen, naturally occurring hormones, are known to modulate the progression and disease outcome of breast cancer [1,2,3]. Approximately 70% of breast cancer patients—positive for estrogen receptor (ER) and progesterone receptor (PR)—receive hormone therapy, such as blocking ER to inhibit estrogen signaling, as the first-line treatment for patients with luminal breast cancer [4, 5]. Previous studies have highlighted the beneficial effects of the progesterone-high luteal phase on surgical outcomes in patients with breast cancer [6,7,8]. However, how progesterone modulates the downstream signaling remains sparsely understood.

The role of ER has been extensively studied in breast cancer due to its prognostic significance [9, 10], along with its role in increasing the invasion and migration of breast cancer cells [11]. The PR, on the other hand, is a known ER target. The presence of PR is described as an indication of ER activity [12]. In vitro studies suggest that progesterone inhibits the invasion and migration of breast cancer cells [13, 2: Table S1.

For luciferase assay, 293FT cells (50,000 cells/well) were co-transfected with pGL3-DSCAM-AS1 (wild-type/mutant) along with pcDNA3.1(-)-miR-130a or pcDNA3.1 empty vector using Lipofectamine 3000 kit. Additionally, co-transfections were performed with pGL3-3’-UTR-ESR1, pcDNA3.1(-)-miR-130a, or pcDNA3.1 empty vector. pEGFP-N2 was transfected to measure transfection efficiency in all wells. Cells were lysed 48 h post-transfection, and luciferase activity was measured using a luminometer (Berthold Luminometer, Germany). Luminescence and fluorescence units were measured from each transfected well. The luciferase activity was calculated by normalization of luminescence units with fluorescence units from the same well and plotted as luciferase activity. Each experiment was performed in triplicates.

Gene–miRNA correlation analysis

The total RNA and miRNA sequencing data for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA). Data from 751 breast cancer samples sequenced for total RNA and miRNAs were considered for further analysis. The samples with normally distributed DSCAM-AS1 or ESR1 expression values were segregated into quartiles. The upper and lower quartile samples were compared. The miRNA levels were compared between patients with ESR1-high and -low expression (the upper and lower quartiles, respectively). A similar analysis was performed for miRNAs in patients with DSCAM-AS1-high and -low expression. The significance of differences between both the groups was calculated using the Wilcoxon–Mann–Whitney test.

Survival analysis

The TCGA breast cancer samples with high and low miRNA expression were compared for survival outcomes. The KM plotter [52] and GEPIA [53] were used for Kaplan–Meier survival analysis within specified breast cancer groups. Overall and relapse-free survival of patients was calculated based on the levels of lncRNAs and miRNAs in the samples.

Statistical analysis

GraphPad Prism version 8 (GraphPad Software, La Jolla, CA) was used to calculate statistical significance between different experimental groups in qPCR, cell-based assays, and luciferase reporter assays. The student's unpaired t-test was used to investigate statistical significance. A p-value < 0.05 was considered to be statistically significant.

Results

We previously reported that progesterone inhibits breast cancer invasion and migration via the deactivation of several kinases [13, 29, 40]. Here, we describe the regulatory role of non-coding RNAs in response to progesterone to mediate the cellular changes.

Identifying significantly deregulated lncRNAs in response to progesterone in breast cancer

First, we analyzed 30 whole transcriptome datasets to identify differentially expressed lncRNAs upon progesterone treatment. Of the 30 tumor samples, 10 had received a single 500 mg dose of hydroxyprogesterone and 20 were controls [37, 54]. Sequencing of these samples generated 17.2–60.7 million reads per sample (median, 37.4 million), wherein > 94–96% reads aligned to the human genome. Differential gene expression analysis between the control and progesterone-treated patients aided in identifying 2,222 differentially expressed genes (FDR < 0.1; 764 up- and 1,458 down-regulated), containing 537 lncRNAs (287 up- and 250 down-regulated), while a majority of the deregulated genes were of protein-coding category (Fig. 1A, Additional file 2: Table S2).

Fig. 1
figure 1

Progesterone deregulates long non-coding RNAs in breast cancer cells. A List of significantly deregulated lncRNAs in the transcriptome sequencing data of breast cancer cell lines and patient samples treated with progesterone. Expression fold change upon progesterone treatment is indicated against each lncRNA. lncRNAs downregulated in primary tumors upon progesterone treatment are highlighted in gray shade. *Represents no expression of the gene in breast primary tumor samples. B–D Real-time PCR analysis of differentially expressed lncRNAs in B T47-D, C BT-474, and D MDA-MB-231 breast cancer cells treated with progesterone. The expression of lncRNAs is normalized with that of GAPDH in the same sample. Changes in the normalized expression of lncRNAs upon treatment are plotted as relative fold change (2−ΔΔCT) with respect to expression in vehicle control for the same cell line. This consists of data from three biological replicates. The horizontal black line represents a normalized expression of lncRNAs in vehicle-treated cells. SGK1, a progesterone-responsive gene, is used as a positive control. p-value calculated using Student’s t-test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns non-significant

Full size image

Further, to better understand the underlying mechanisms of action of progesterone, we performed whole transcriptome sequencing of T47-D (PR + /ER + /Her2-) and MDA-MB-231 (PR-/ER-/Her2-) breast cancer cells in response to progesterone treatment. A minimum of 60 million pair-end reads were obtained for each sample with > 90% of reads with a Phred score > 30, suggesting good quality of the data. Of 382 and 206 differentially expressed genes in T47-D and MDA-MB-231 cells, respectively, 18 lncRNAs were significantly deregulated in response to progesterone (-1 < log2FC > 1; p-value < 0.05) (Fig. 1A; Additional file 1: Figure S1; Additional file 2: Tables S3, S4). MDA-MB-231, a PR-negative cell line, also showed active transcriptional response to progesterone treatment, likely due to the PR-independent mode of action of progesterone mediated by glucocorticoid receptor (GR) [40]. Interestingly, expression of a few lncRNAs was consistently deregulated in the progesterone-treated breast tumor and cell line transcriptome data, viz.., DSCAM-AS1, PCED1B-AS1, RP11-21L23.2, RP11-363E7.4, and AC012358.8 (Fig. 1A). Of these, expression of DSCAM-AS1 was considerably downregulated in progesterone-treated breast cancer patients transcriptome data. Moreover, the normalized DSCAM-AS1 expression in progesterone untreated samples range from 3–860, compared to 3–450 in progesterone treated samples (Additional file 1: Figure S2). This suggests the variable DSCAM-AS1 expression across progesterone treated and untreated primary breast tumor samples. Further, consistent with our previous study, SGK1 was found to be significantly upregulated [13, 29, 40], in addition to deregulated expression of some lncRNAs, in progesterone-treated breast cancer samples (Fig. 1A). Taken together, the transcriptome analyses of tumor and cell lines identified novel progesterone-responsive lncRNAs in breast cancer.

Progesterone downregulates the expression of DSCAM-AS1 to suppress migration and invasion of PR-positive breast cancer cells

An orthologous validation of the differentially expressed lncRNAs by real-time PCR identified a long non-coding RNA Down syndrome cell adhesion molecule antisense, DSCAM-AS1, as downregulated in ER/PR-positive T47-D and BT-474 cells upon progesterone treatment compared to ER/PR-negative MDA-MB-231 cells (Fig. 1B–D). In contrast to T47D and BT474 cells, we detected no significant change in the expression of DSCAM-AS1 in MCF7 cells (Additional file 1: Figure S3), consistent with its distinct transcriptional landscape, as described earlier, in response to progesterone treatment [18, 55]. The downregulation of DSCAM-AS1 could be effectively blocked by mifepristone, an antagonist of progesterone receptor (PR) and glucocorticoid receptor (GR) (Fig. 2A, B). However, the siRNA-mediated knockdown of PR, but not GR, rescued the down-regulation of DSCAM-AS1 in response to progesterone treatment, suggesting that PR mediates the downregulation of DSCAM-AS1 in response to progesterone in PR-positive cells (Fig. 2C). Next, we tested whether DSCAM-AS1 affects the inhibition of migration and invasion ability of breast cancer cells in response to progesterone [13]. Interestingly, DSCAM-AS1 knockdown could mimic the effect of progesterone by inhibiting breast cancer cell migration and invasion comparable to the extent obtained following treatment of the PR-positive T47-D and BT474 cells with progesterone (Fig. 2D–G).

Fig. 2
figure 2

Progesterone downregulates DSCAM-AS1 via progesterone receptor to suppress invasion and migration of PR-positive breast cancer cells. Real-time PCR analysis of DSCAM-AS1 in response to progesterone and mifepristone + progesterone in A T47-D and B BT-474. DSCAM-AS1 expression is normalized with that of GAPDH, and relative fold change values are plotted in comparison to vehicle control. C Real-time PCR analysis indicating DSCAM-AS1 expression upon PR and GR knockdown followed by progesterone treatment in T47-D cells. Relative fold change is calculated by 2−ΔΔCT and plotted on Y-axis. p-value calculated using Student’s t-test. Transwell D–E cell migration and F–G invasion assay upon siRNA-mediated silencing of DSCAM-AS1 in T47-D and BT-474 cells. Representative images of crystal violet-stained migrated or invaded cells (10 ×) from each condition are shown. Each bar plot indicates percent cell migration or invasion in each condition with respect to vehicle-treated si-control cells. p-value calculated using Student’s t-test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns non-significant

Full size image

DSCAM-AS1 downregulates the expression of ESR1 in response to progesterone in PR-positive breast cancer cells

Estrogen receptor (ER) has previously been shown to regulate DSCAM-AS1 expression via binding near the promoter region [27]. Consistent with the literature, we observed a significantly higher expression of DSCAM-AS1 transcript in TCGA breast cancer patient samples and ER/PR-positive T47-D and BT-474 cells than in MDA-MB-231 cells (Fig. 3A, B, Additional file 1: Figure S4). We hypothesized that ER/PR could modulate the DSCAM-AS1 expression in response to progesterone by binding to its upstream regulatory or distal regions. We analyzed chromatin immunoprecipitation ChIP-sequencing data following progesterone treatment in PR-positive T47-D cells, as described earlier [18, 40]. We identified enrichment of PR, ER, and p300 binding peak upon progesterone treatment at the “region 3” regulatory sequence of DSCAM-AS1 (Additional file 1: Figure S5). This suggests that progesterone alters the binding occupancy of PR and ER near DSCAM-AS1. Surprisingly, siRNA-mediated knockdown of DSCAM-AS1 in turn led to a significant decrease in the expression of ESR1 transcript, comparable to progesterone treatment, suggesting a possible feedback mechanism by which DSCAM-AS1 regulates the expression of ESR1 in T47-D and BT-474 cells (Fig. 3C, D). In contrast, overexpression of DSCAM-AS1 in T47-D cells, but not MDA-MB-231 cells, led to overexpression of ESR1 (Fig. 3E–H), suggesting that progesterone reduces expression of DSCAM-AS1 that further suppresses expression of ESR1 to inhibit cell migration and invasion in PR-positive breast cancer cells.

Fig. 3
figure 3

DSCAM-AS1 regulates ESR1 levels similar to progesterone treatment in PR-positive breast cancer cells. Real-time PCR analysis indicating normalized expression levels of A DSCAM-AS1 and B ESR1 in breast cancer cell lines with different receptor statuses. Delta Ct value (expression of DSCAM-AS1 or ESR1 normalized to that of GAPDH) is plotted on Y-axis. The p-value is calculated using the student's t-test. C, D Real-time PCR analysis indicating expression of ESR1 in C T47-D and D BT-474 cells upon siRNA-mediated silencing of DSCAM-AS1 and progesterone treatment. Relative fold change with respect to si-control and vehicle treatment is plotted. The expression of ESR1 is normalized to that of ACTB. p-value calculated using Student’s t-test. E, F Real-time PCR analyses indicate expression of E DSCAM-AS1 and F ESR1 in T47-D cells upon stable overexpression of DSCAM-AS1. Relative fold change in expression of DSCAM-AS1 and ESR1 with respect to that of ACTB is plotted. p-value calculated using Student’s t-test. G, H Real-time PCR analyses indicate expression of G DSCAM-AS1 and H ESR1 in MDA-MB-231 cells upon transient overexpression of DSCAM-AS1. Relative fold change in expression of gene with respect to that of ACTB is plotted. p-value calculated using Student’s t-test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns non-significant

Full size image

DSCAM-AS1 sponges miR-130a targeting 3’-UTR of ESR1 to suppress migration and invasion of PR-positive breast cancer cells

LncRNAs are known to sponge miRNAs, and thus, reduce the availability of miRNAs for target gene suppression [56, 57]. We thus tested whether DSCAM-AS1 could sponge miRNAs targeting the 3’-UTR of ESR1. Using the DIANA-LncBase v2 database prediction module, we identified 167 miRNAs that could bind DSCAM-AS1 with miTG-score > 0.7 (Additional file 2: Table S5). Concomitantly, we identified 72 miRNAs predicted to target the 3’-UTR of ESR1 from the miRTarBase database (Additional file 2: Table S6), with 9 overlap** miRNAs, viz. miR-548x, miR-548aj, miR-335, miR-129, miR-4422, miR-3121, miR-193b, miR-130a, and miR-301a (Fig. 4A). A real-time PCR-based validation of these 9 miRNAs in response to progesterone or genetic knockdown of DSCAM-AS1 identified miR-130a as significantly upregulated in T47-D and BT-474 cells (Fig. 4B, Additional file 1: Figure S6). Interestingly, in BT474 cells, a greater number of miRNAs were downregulated in response to silencing DSCAM-AS1 than in response to progesterone treatment. This may be related to the de-repression of miRNAs upon silencing DSCAM-AS1, a miRNA sponge, as well as the activation and inhibition of various pathways in response to progesterone, such as the up-regulation of miR-129–2, which in turn regulates the expression of PR, as demonstrated before [29].

Fig. 4
figure 4

DSCAM-AS1 sponge miR-130a targeting 3’-UTR of ESR1 to suppress migration and invasion of PR-positive breast cancer cells. A Venn diagram depicting miRNAs predicted to target DSCAM-AS1 and 3’-UTR of ESR1. B Heatmap representation of real-time PCR analyses indicating significance (p-values) of fold change in the 9 miRNAs in response to progesterone and upon silencing DSCAM-AS1 in T47-D and BT-474 cells. Gray boxes indicate significant up-regulation, while, white boxes indicate no significant change in expression of the miRNAs. The relative expression fold change for miRNAs in response to progesterone is determined with respect to expression in vehicle control or siRNA-control sample. p-value calculated using Student’s t-test. C, D Real-time PCR analyses indicating expression of miR-130a in C T47-D and D MDA-MB-231 cells upon overexpression of DSCAM-AS1. Expression of miR-130a is normalized with respect to that of U6. p-value calculated using Student’s t-test. E–H Transwell cell invasion and migration assay with ectopic overexpression of miR-130a in E, F T47-D and G, H BT-474 cells along with progesterone treatment. Cells transfected with an empty vector are used as a control for comparison. Ethanol vehicle treatment is included to compare with progesterone treatment conditions. Percent cell invasion and migration are plotted. p-value calculated using Student’s t-test. I miR-130a binding region sequence in DSCAM-AS1-wild type and DSCAM-AS1-mutant. Luciferase reporter activity quantification by co-transfecting plasmids indicated by “ + ”. Relative luciferase activity is calculated by normalizing individual luciferase activity with the GFP signal emitted from the same well. p-GL3-DSCAM-AS1-Mutant has the miR-130a binding site mutated. The assay is performed in three biological replicates. p-value calculated using Student’s t-test. J, K Real-time PCR analysis indicating expression of miR-130a upon DSCAM-AS1 transient overexpression in J T47D and K MDA-MB-231 cells. Expression of miR-130a is normalized with respect to that of U6. p-value calculated using Student’s t-test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns non-significant

Full size image

Next, to investigate the function, we ectopically expressed miR-130a in T47-D and BT-474 cells. The ectopic expression of miR-130a led a significant decrease in ESR1 transcript than in vector control (Fig. 4C, D), with a concomitant decrease in invasion and migration of T47-D and BT-474 cells. miR-130a overexpression could mimic progesterone treatment or DSCAM-AS1 knockdown in PR-positive T47-D and BT-474 cells (Fig. 4E–H). Furthermore, to test a direct interaction between miR-130a and DSCAM-AS1, DSCAM-AS1 cDNA was cloned downstream to the luciferase reporter gene. The findings revealed a decrease in normalized luciferase activity upon overexpression of wild-type miR-130a, but not with miR-130a construct with a mutated binding site. As a positive control, the 3’-UTR of ESR1 cloned downstream to the luciferase gene showed similar inhibition of luciferase activity (Fig. 4I). In contrast, overexpression of DSCAM-AS1 in T47-D, but not MDA-MB-231, cells led to a significant reduction in miR-130a levels than that in vector control (Fig. 4J, K, Additional file 1: Figure S7 A-C). Interestingly, miR-130a also showed a significant inverse correlation to DSCAM-AS1 and ESR1 expression in 752 TCGA breast cancer samples (Additional file 1: Figure S8 A-B). Taken together, these results validate the association between DSCAM-AS1 and miR-130a to maintain ESR1 levels in PR-positive breast cancer cells with a consistent inverse correlation of miR-130a with the expression of DSCAM-AS1 and ESR1 in the TCGA patient samples.

Upregulation of miR-130a correlates with better survival outcome in breast cancer patients

The prognostic value of DSCAM-AS1 and miR-130a expression in survival prediction was further tested in TCGA breast cancer datasets (n = 1062) generated by whole transcriptome sequencing to perform the Kaplan–Meier (KM) survival analysis. Patients in the datasets were divided into high- and low-expression classes by the median expression value of DSCAM-AS1 and miR-130a, and a log-rank test was performed for stratifying patients with different prognoses. The analysis showed a significantly better overall survival in patients with breast carcinoma with high miR-130a expression than those with low miR-130a expression (log-rank p = 0.02). Patients with high expression of miR-130a survived better (87 months) than those with low expression of miR-130a (69 months). Overall, we observed a survival benefit of 18 months in the miR-130a high expression cohort. Similar results were observed in patients with ER-positive subtype cancer (log-rank p = 0.05) (Fig. 5A, B). In contrast, KM analysis of patients with breast cancer did not show statistically significant change in overall survival in patients who exhibit high and low levels of DSCAM-AS1 (Fig. 5C, D). These findings imply that a high expression of miR-130a influence survival of patients with breast cancer.

Fig. 5
figure 5

Upregulation of miR-130a correlates with a tendency toward better survival outcome in breast cancer patients. A, B Kaplan–Meier (KM) survival curves indicate differences in overall survival based on miR-130a high and low levels in patients with breast carcinoma (BRCA) of A all subtypes and B estrogen receptor (ER)-positive subtype. C, D KM survival plots indicate differences in overall survival based on DSCAM-AS1 high and low levels in patients with C all BRCA subtypes and D ER-positive subtypes. The probability of survival is plotted on Y-axis and survival time (in months) is represented on X-axis. The red curve represents survival probability in patients with high expression; whereas, the black curve represents that in patients with low expression. Log-rank p < 0.05 is considered the cutoff for calculating the significance value. The hazard ratio (HR) for each KM plot is also denoted

Full size image

Discussion

Progesterone confers better survival outcomes in patients with breast cancer, especially in those with lymph node involvement [58]. These early clinical observations have increased interest in researchers globally to investigate the mechanisms by which progesterone affects breast cancer pathophysiology. We have previously shown that progesterone reduces breast cancer cell invasion and migration [13] by regulating a tight network of protein-coding genes that reduce the activity of kinases that are known to induce cellular stress [40]. The present study highlights the multiplicity of genomic mediators, especially ncRNAs, recruited by progesterone and PR in breast cancer to abrogate cell invasion and migration.

To begin with, this is the first study to describe progesterone-responsive lncRNAs in breast tumor samples and cell lines. Interestingly, the analyses identified DSCAM-AS1 as a novel target of progesterone in breast cancer. Progesterone downregulates the expression of DSCAM-AS1 specifically in PR-positive breast cancer cells, wherein PR modulates the genomic binding pattern of ER, the classical activator of DSCAM-AS1 [27], in response to progesterone. This also highlights the importance of PR in clinical outcome of breast cancer prognosis and confirms the previous findings that PR modulates ER binding in breast cancer cells treated with progesterone [18, 59]. However, recent report suggests that progesterone treatment may have varied response on tumor growth in patient derived xenograft mouse models [60]. Consistent with this, we also observed variability in DSCAM-AS1 expression in response to progesterone.

Second, the findings suggest that DSCAM-AS1 functions as a miRNA sponge to help maintain the high expression of ER in breast cancer cells. DSCAM-AS1 has previously been shown to function as a miRNA sponge for miR-101 [61] and miR-186 [62] in osteosarcoma, and miR-136 in endometrial cancer [63]. Interestingly, we show that progesterone opposes the DSCAM-AS-1–ESR1 feedback loop, and thus essentially the ER signaling pathway, by employing two synergistic mechanisms—it decreases the expression of DSCAM-AS1 and increases the expression of miR-130a that binds to both DSCAM-AS1 and 3’UTR of ESR1 in breast cancer cells. This strengthens the role of progesterone in regulating the expression of non-coding genomic elements in breast cancer [29, 64], in addition to regulating the expression of protein-coding elements. The results of the present study also emphasize the necessity of PR expression in breast cancer cells for progesterone to alter the expression of DSCAM-AS1 and miR-130a, as these effects were not observed in PR-negative MDA-MB-231. Additionally, the expression pattern of miR-130a was found to be inversely correlated with that of ESR1 and DSCAM-AS1 in cell lines and patients with breast cancer.

Third, the cellular experiments indicated that silencing of DSCAM-AS1 or overexpression of miR-130a led to a significant reduction in breast cancer cell migration and invasion than that in vehicle control cells, comparable to the effect induced by progesterone-alone. Furthermore, progesterone treatment of cells with high miR-130a levels led to a greater reduction in cell invasion and migration than progesterone treatment of vehicle-treated control cells; this result demonstrates that variation in expression of these ncRNAs modifies other genomic components that augment the effects of progesterone on breast cancer cells, as described previously [13, 29, 40]. Further, miR-130a has been reported to be involved in mitigating progression in breast cancer stem cells [65], and its expression has been reported to be downregulated in breast cancer [66, 67]. Finally, using the TCGA datasets, we show that patients with breast cancer with high miR-130a levels correlate with a tendency toward better overall survival (that could not attain statistical significance). Therefore, the findings may help clinicians to better categorize patients with luminal A/B subtype based on the expression of DSCAM-AS1 or miR-130a to receive appropriate care and aid in prolonging their survival outcomes.

In conclusion, this study elucidates an underlying mechanism for a clinical consequence in response to progesterone treatment among patients with breast cancer. Progesterone downregulates the expression of DSCAM-AS1, a known ncRNA member of the ER signaling pathway, and increases the expression of miR-130a that inhibits ESR1, to suppress breast cancer cell invasion and migration. Additionally, high miR-130a levels are associated with improved overall survival outcomes in patients with breast cancer, similar to that observed in the randomized controlled trial with preoperative progesterone. Thus, progesterone treatment under hormonal therapy in the adjuvant and neoadjuvant settings may help in impeding cell migration and invasion of breast cancer cells, and in improving the overall and relapse-free survival outcomes in patients with breast cancer.